LAmp: Identifying Load-Bearing Assumptions for Outcome-Sensitive Clarification

Language agents routinely act on underspecified instructions by silently committing to one interpretation. Yet ambiguity alone does not determine whether clarification is necessary: different valid readings can lead to the same task outcome. We call an unresolved assumption load-bearing when resolving it differently can materially change the task outcome. Good clarification thus requires coverage (asking about load-bearing assumptions) and selectivity (not asking unnecessarily). We introduce LAmp (Load-bearing AssuMPtion), a clarification framework that detects unresolved assumptions, enumerates their readings, and decides which are load-bearing, using structured reasoning or counterfactual execution. Since ambiguity annotations do not reveal whether the outcome materially changes, we construct execution-grounded evaluation cases: starting from a task that a fixed agent solves correctly, we remove one specification detail and rerun the agent; an observed outcome change yields a load-bearing evaluation target, while outcome preservation yields a negative control for the tested context. On text-to-SQL (BIRD), LAmp recovers 95% of load-bearing evaluation targets, versus 72% for the strongest prior method, and counterfactual execution makes clarification more selective at nearly the same coverage. The formulation transfers to software engineering (SWE-bench) without domain-specific adaptation, and the clarification policy can be distilled into a 7B model while keeping the execution agent fixed. Together, these results suggest that agents should not request clarification whenever an instruction is ambiguous, but when resolving the ambiguity differently can change the task outcome.